Penerapan Cuckoo Search Algorithm (CSA) untuk Menyelesaikan Uncapacitated Facility Location Problem (UFLP)

The aim of this research is to solve Uncapacitated Facility Location Problem (UFLP) using Cuckoo Search Algorithm (CSA). UFLP involves n locations and facilities to minimize the sum of the fixed setup costs and serving costs of m customers. In this problem, it is assumed that the built facilities have no limitations in serving customers, all request from each customers only require on facility, and one location only provides one facility. The purpose of the UFLP is to minimize the total cost of building facilities and customer service costs. CSA is an algorithm inspired by the parasitic nature of some cuckoo species that lay their eggs in other host birds nests. The Cuckoo Search Algorithm (CSA) application program for resolving Uncapacitated Facility Location Problems (UFLP) was made by using Borland C ++ programming language implemented in two sample cases namely small data and big data. Small data contains 10 locations and 15 customers, while big data consists 50 locations and 50 customers. From the computational results, it was found that higher number of nests and iterations lead to minimum total costs. Smaller value of pa brought to better solution of UFLP.

Solving Uncapacitated Facility Location Problem Using Heuristic Algorithms

A well-known combinatorial optimization problem, known as the uncapacitated facility location problem (UFLP) is considered in this article. A deterministic heuristic algorithm and a randomized heuristic algorithm are presented to solve UFLP. Though the proposed deterministic heuristic algorithm is very simple, it produces good solution for each instance of UFLP considered in this article. The main purpose of this article is to process all the data sets of UFLP available in the literature using a single algorithm. The proposed two algorithms are applied on these test instances of UFLP to determine their effectiveness. Here, the solution obtained from the proposed randomized algorithm is at least as good as the solution produced by the proposed deterministic algorithm. Hence, the proposed deterministic algorithm gives upper bound on the solution produced by the randomized algorithm. Although the proposed deterministic algorithm gives optimal results for most of the instances of UFLP, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this article including those for which the deterministic algorithm fails to achieve the optimal solutions.